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“Know your customers and give them what they want” is the fundamental principle of marketing.

This principle is simple in theory, but increasingly challenging to put into practice. Short of being a mind reader or having a crystal ball, it's difficult for marketers to know what's on a customer's mind today, or anticipate what the customer may need or want tomorrow.

The challenge doesn't stem from lack of customer data. The fact is, customers and prospects are giving us information about themselves all the time. Through every response, customer contact, event, transaction and Web site hit, they reveal something about themselves.

Databases are chock full of these useful tidbits, and call centers and other customer management systems are overflowing with details about customers and contacts. The challenge is that raw data does not have value per se; it needs to be turned into useful information.

That is where analytical technology comes into play. A philosopher once wrote that finding the patterns in the randomness of life is the way we create beauty and make art. A similar statement could be made about analytics, which find patterns in the randomness of data so that you can discover valuable information and gain insight.

An array of analytical products is available for desktop and enterprise systems and for pros and novices alike.

Generally, analytics fall into four categories:

  1. Statistical analysis 
  2. On-line analytical processing (OLAP) 
  3. Data mining 
  4. Text mining

Statistical analysis refers to a collection of methods used to process large amounts of data to uncover key facts, patterns and trends.

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Colin Shearer is vice-president of customer analytics at SPSS Inc. (www.spss.com), a global provider of predictive analytics.